Longitudinal trajectories of cognitive change in preclinical Alzheimer's disease: a growth mixture modeling analysis

Difference in trajectories according to early amyloid accumulation in cognitively unimpaired elderly

BACKGROUND AND PURPOSE

Amyloid β (Aβ), a major biomarker of Alzheimer's disease, leads to tau accumulation, neurodegeneration and cognitive decline. Modelling the trajectory of Aβ accumulation in cognitively unimpaired (CU) individuals is crucial, as treatments targeting Aβ are anticipated. The evolution of Aβ levels was investigated to determine whether it could lead to classification into different groups by studying longitudinal Aβ changes in older CU individuals, and differences between the groups were compared.

METHODS

A total of 297 CU participants were included from the Alzheimer's Disease Neuroimaging Initiative database, and these participants underwent apolipoprotein E (APOE) genotyping, neuropsychological testing, brain magnetic resonance imaging, and an average of 3.03 follow-up 18F-florbetapir positron emission tomography scans. Distinct Aβ trajectory patterns were classified using latent class growth analysis, and longitudinal cognitive performances across these patterns were assessed with a linear mixed effects model.

RESULTS

The optimal model consisted of three classes, with a high entropy value of 0.947. The classes were designated as follows: class 1, non-accumulation group (n  = 197); class 2, late accumulation group (n  = 70); and class 3, early accumulation group (n  = 30). The late accumulation and early accumulation groups had more APOE ε4 carriers than the non-accumulation group. The longitudinal analysis of cognitive performance revealed that the early accumulation group showed the steepest decline (modified Preclinical Alzheimer's Cognitive Composite with digit symbol substitution [mPACCdigit], p < 0.001; modified Preclinical Alzheimer's Cognitive Composite with trails B [mPACCtrailsB], p < 0.001) and the late accumulation group showed a steeper decline (mPACCdigit, p = 0.014; mPACCtrailsB, p = 0.007) compared to the non-accumulation group.

CONCLUSIONS

Our study showed the heterogeneity of Aβ accumulation trajectories in CU older individuals. The prognoses for cognitive decline differ according to the Aβ trajectory patterns.

Functional aging trajectories of older cancer survivors: a latent growth analysis of the US Health and Retirement Study

PURPOSE

We aimed to identify prototypical functional aging trajectories of US cancer survivors aged 50 and older, overall and stratified by sociodemographic and health-related characteristics.

METHODS

Data were from 2986 survivors of a first incident cancer diagnosis (except non-melanoma skin cancer) after age 50 in the population representative U.S. Health and Retirement Study from 1998-2016. Cancer diagnoses, episodic memory function, and activity of daily living (ADL) limitations were assessed at biennial study interviews. Using time of cancer diagnosis as the baseline, we used group-based trajectory modeling to identify trajectories of memory function and ADL limitations following diagnosis.

RESULTS

We identified five memory loss trajectories (high: 8.4%; medium-high: 18.3%; medium-low: 21.5%; low: 25.5%; and, very low: 26.2%), and four ADL limitation trajectories (high/increasing limitations: 18.7%; medium limitations: 18.7%; low limitations: 8.14%; no limitations: 60.0). The high memory loss and high/increasing ADL limitation trajectories were both characterized by older age, being female (52% for memory, 58.9% for ADL), having lower pre-cancer memory scores, and a higher prevalence of pre-cancer comorbidities including stroke (30.9% for memory and 29.7% for ADL), hypertension (64.7% for memory and 69.8 for ADL), and depressive symptoms. In joint analyses, we found that generally those with higher memory were more likely to have fewer ADL limitations and vice versa.

CONCLUSION

Older cancer survivors experience heterogeneous trajectories of functional aging that are largely characterized by comorbidities prior to diagnosis.

IMPLICATION FOR CANCER SURVIVORS

Results can help identify older cancer survivors at increased risk for accelerated functional decline.

Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression

Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-β (Aβ)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aβ42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.

Classification and prediction of cognitive trajectories of cognitively unimpaired individuals

Objectives

Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts.

Methods

A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model.

Results

Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model).

Conclusion

Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.

Characterizing the individual course of health-related quality of life after subarachnoid haemorrhage: Latent growth mixture modelling

BACKGROUND

Survivors of aneurysmal subarachnoid haemorrhage (SAH) show heterogeneous profiles of health-related quality of life (HrQoL). The aim of this study was to characterize individual differences in the course of HrQoL following SAH using latent growth mixture modelling (LGMM).

METHODS

A longitudinal study with 113 incident cases of aneurysmal SAH was performed in order to evaluate clinical outcome (Hunt and Hess scale, Barthel-Index, Beck Depression Inventory) and HrQoL data (EQ-5D) at baseline, 6 and 12 months. The heterogeneity in HrQoL courses after SAH was analysed using LGMM.

RESULTS

Four subgroups (classes) of different patterns of HrQoL course after SAH were identified. Two of these classes (1 and 3) comprised patients with considerably reduced initial HrQoL, which was associated with more severe symptoms of SAH. Class 1 showing the worst EQ5D-index values during the entire study period. Class 3 experiencing a considerable improvement in HrQoL values. In comparison to classes 1 and 3, class 2 and 4 were characterized by less severe SAH and better functional outcome. An important difference in the disease course between classes 2 and 4 was a temporary increase in depression scores at the 6-month time point in class 4, which was associated with a considerable reduction in HrQoL.The specific clinical parameters characterizing differences between classes, such as severity of SAH, functional outcome, cognitive impairment and post-stroke depression, were identified and the influence of their potential improvement on HrQoL was estimated.

CONCLUSION

By means of LGMM we could classify the course of HrQoL after SAH in four different patterns, which are relevant for the clinical decisions. Clinical parameters, which can be modified in order to improve the course of HrQoL were identified and could help to develop individual therapeutic strategies for the rehabilitation after SAH.

A systematic review of subjective cognitive characteristics predictive of longitudinal outcomes in older adults

BACKGROUND AND OBJECTIVES

Subjective cognitive decline (SCD) is a common experience of self-perceived decline without objective cognitive impairment among older adults. SCD has been conceptualized as very early Alzheimer's disease (AD), but the specific SCD features predictive of clinical or cognitive decline remain unclear. This systematic review is the first to characterize specific SCD features and their relation to longitudinal outcomes.

RESEARCH DESIGN AND METHODS

Multiple electronic databases were searched from inception until August 2021 for longitudinal studies of adults aged >50 (mean>60) and free of dementia, with baseline SCD measurement and clinical or cognitive follow-up. Studies were screened for inclusion criteria and assessed for risk of bias using weight-of-evidence ratings.

RESULTS

570 potentially relevant studies were identified, and 52 studies evaluated for eligibility after initial screening. Thirty-three studies with medium to high weight-of-evidence ratings were included and results narratively synthesized. Measurement methods varied substantially across studies: the majority (n=27) assessed SCD symptom types and intensity, and consistently reported that higher symptom burden increased the risk for MCI and dementia. The evidence was less compelling for cognitive outcomes. A handful of studies (n=5) suggested a predictive role for SCD symptom consistency and informant corroboration.

DISCUSSION AND IMPLICATIONS

SCD symptom intensity emerged from our review as the most reliable predictor of future clinical outcomes. Combinations of SCD-Plus symptoms also had predictive utility. No single symptom was uniquely prognostic. Our findings support the quantitative evaluation of SCD symptoms in the assessment of risk for progression to MCI or dementia.

Predicting the long-term cognitive trajectories using machine learning approaches: A Chinese nationwide longitudinal database

OBJECTIVES

This study aimed to explore the long-term cognitive trajectories and its' determinants, and construct prediction models for identifying high-risk populations with unfavorable cognitive trajectories.

METHODS

This study included 3502 older adults aged 65-105 years at their first observations in a 16-year longitudinal cohort study. Cognitive function was measured by the Chinese version Mini Mental State Examination. The heterogeneity of cognitive function was identified through mixed growth model. Machine learning algorithms, namely regularized logistic regression (r-LR), support vector machine (SVM), random forest (RF), and super learner (SL) were used to predict cognitive trajectories. Discrimination and calibration metrics were used for performance evaluation.

RESULTS

Two distinct trajectories were identified according to the changes of MMSE scores: intact cognitive functioning (93.6%), and dementia (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low-frequency leisure activities, and low baseline BADL score were associated with a rapid decline in cognitive function. r-LR, SVM, and SL performed well in predicting cognitive trajectories (Sensitivity: 0.73, G-mean: 0.65). Age and psychological well-being were key predictors.

CONCLUSION

Two cognitive trajectories were identified among older Chinese, and the identified key factors could be targeted for constructing early risk prediction models.

Telephone Interview for Cognitive Status Scores Associate with Cognitive Impairment and Alzheimer's Disease Pathology at Death

BACKGROUND

Early diagnosis of Alzheimer's disease (AD) provides an opportunity for early intervention. Cognitive testing has proven to be a reliable way to identify individuals who may be at risk of AD. The Telephone Assessment for Cognitive Screening (TICS) is proficient in screening for cognitive impairment. However, its ability to identify those at risk of developing AD pathology is unknown.

OBJECTIVE

We aim to investigate associations between TICS scores, collected over a period of 13 years, and the cognitive status of participants at death. We also examine relationships between TICS scores and neuropathological indices of AD (CERAD score, Thal phase, and Braak stage).

METHODS

Between 2004 and 2017, participants from The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age underwent cognitive assessment using TICS. Scores from four time points were available for analysis. Cognitive impairment and AD pathology at death was evaluated in 101 participants.

RESULTS

TICS scores at time points 2, 3, and 4 were significantly lower in those cognitively impaired at death compared to those considered cognitively normal. There were significant negative correlations between TICS scores and CERAD score and Braak stage at time points 2 and 4. No correlations between Thal phase and TICS were found.

CONCLUSION

Findings indicate that TICS could be used not only to screen for cognitive impairment, but also to identify individuals at risk of developing AD pathology, many years before any overt symptoms occur. Once identified, 'at risk' individuals could be targeted for early interventions which could attenuate the progression of the disease.

Ambient Air Pollution and Long-Term Trajectories of Episodic Memory Decline among Older Women in the WHIMS-ECHO Cohort

BACKGROUND

Episodic memory decline varies by age and underlying neuropathology. Whether ambient air pollution contributes to the heterogeneity of episodic memory decline in older populations remains unclear.

OBJECTIVES

We estimated associations between air pollution exposures and episodic memory decline according to pollutant, exposure time window, age, and latent class subgroups defined by episodic memory trajectories.

METHODS

Participants were from the Women's Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes. Older women (n = 2,056 ; 74-92 years of age) completed annual (2008-2018) episodic memory assessments using the telephone-based California Verbal Learning Test (CVLT). We estimated 3-y average fine particulate matter [PM with an aerodynamic diameter of ≤ 2.5 μ m (PM 2.5 )] and nitrogen dioxide (NO 2 ) exposures at baseline and 10 y earlier (recent and remote exposures, respectively), using regionalized national universal kriging. Separate latent class mixed models were used to estimate associations between interquartile range increases in exposures and CVLT trajectories in women ≤ 80 and > 80  years of age , adjusting for covariates.

RESULTS

Two latent classes were identified for women ≤ 80  years of age (n = 828 ), "slow-decliners" {slope = - 0.12 / y [95% confidence interval (CI): - 0.23 , - 0.01 ] and "fast-decliners" [slope = - 1.79 / y (95% CI: - 2.08 , - 1.50 )]}. In the slow-decliner class, but not the fast-decliner class, PM 2.5 exposures were associated with a greater decline in CVLT scores over time, with a stronger association for recent vs. remote exposures [- 0.16 / y (95% CI: - 2.08 , - 0.03 ) per 2.88   μ g / m 3 and - 0.11 / y (95% CI: - 0.22 , 0.01) per 3.27   μ g / m 3 , respectively]. Among women ≥ 80  years of age (n = 1,128 ), the largest latent class comprised "steady-decliners" [slope = - 1.35 / y (95% CI: - 1.53 , - 1.17 )], whereas the second class, "cognitively resilient", had no decline in CVLT on average. PM 2.5 was not associated with episodic memory decline in either class. A 6.25 -ppb increase in recent NO 2 was associated with nonsignificant acceleration of episodic memory decline in the ≤ 80 -y-old fast-decliner class [- 0.21 / y (95% CI: - 0.45 , 0.04)], and in the > 80 -y-old cognitively resilient class [- 0.10 / y (95% CI: - 0.24 , 0.03)] and steady-decliner class [- 0.11 / y (95% CI: - 0.27 , 0.05)]. Associations with recent NO 2 exposure in women > 80  years of age were stronger and statistically significant when 267 women with incident probable dementia were excluded [e.g., - 0.12 / y (95% CI: - 0.22 , - 0.02 ) for the cognitively resilient class]. In contrast with changes in CVLT over time, there were no associations between exposures and CVLT scores during follow-up in any subgroup.

DISCUSSION

In a community-dwelling U.S. population of older women, associations between late-life exposure to ambient air pollution and episodic memory decline varied by age-related cognitive trajectories, exposure time windows, and pollutants. https://doi.org/10.1289/EHP7668.

Differences in Trajectories and Predictive Factors of Cognition over Time in a Sample of Cognitively Healthy Adults, in Zaragoza, Spain

Great inter-individual variability has been reported in the maintenance of cognitive function in aging. We examined this heterogeneity by modeling cognitive trajectories in a population-based longitudinal study of adults aged 55+ years. We hypothesized that (1) distinct classes of cognitive trajectories would be found, and (2) between-class differences in associated factors would be observed. The sample comprised 2403 cognitively healthy individuals from the Zaragoza Dementia and Depression (ZARADEMP) project, who had at least three measurements of the Mini-Mental State Examination (MMSE) in a 12-year follow-up. Longitudinal changes in cognitive functioning were modeled using growth mixture models (GMM) in the data. The best-fitting age-adjusted model showed 3 distinct trajectories, with 1-high-to-moderate (21.2% of participants), 2-moderate-stable (67.5%) and, 3-low-and-declining (9.9%) cognitive function over time, respectively. Compared with the reference 2-trajectory, the association of education and depression was significantly different in trajectories 1 and 3. Instrumental activities of daily living (iADLs) were only associated with the declining trajectory. This suggests that intervention strategies should be tailored specifically to individuals with different trajectories of cognitive aging, and intervention strategies designed to maintain cognitive function might be different from those to prevent decline. A stable cognitive performance ('successful cognitive aging') rather than a mild decline, might be more 'normal' than generally expected.

Empirically defining the preclinical stages of the Alzheimer's continuum in the Alzheimer's Disease Neuroimaging Initiative

AIM

The National Institute on Aging and the Alzheimer's Association published new research criteria defining the Alzheimer's continuum (AC) by the presence of positive amyloid-β biomarkers. Symptom severity of those on the AC is staged across six levels, including two preclinical stages (stages 1 and 2). AC stage 2 is defined by the presence of at least one of the following: (i) transitional cognitive decline; (ii) subjective cognitive decline; or (iii) neurobehavioural symptoms. In contrast, AC stage 1 is defined by the absence of symptoms.

METHODS

Initial empirical definitions for each symptom class were developed. These empirical criteria were then applied in a sample of 285 cognitively normal, amyloid-positive individuals from the Alzheimer's Disease Neuroimaging Initiative for purposes of AC stage 1 and 2 classification.

RESULTS

In this sample, 56.10% of participants were asymptomatic and classified as AC stage 1. In contrast, 42.46% of individuals were positive for at least one symptom class: 22.11% for transitional cognitive decline, 20.35% for subjective cognitive decline, and 14.74% for neurobehavioural symptoms. AC stage was a predictor of cognitive/functional decline over 4 years of follow up in a longitudinal growth model (B = 0.33, P < 0.001).

CONCLUSIONS

Results provide a methodology to operationalize the National Institute on Aging and the Alzheimer's Association AC stage 1 and 2 criteria and include preliminary evidence of the validity of this approach. The methods outlined in this manuscript can be used to test hypotheses regarding prodromal Alzheimer's disease, as well as implemented in clinical trial selection procedures.

Dynamic latent variable models for the analysis of cognitive abilities in the elderly population

Cognitive functioning is a key indicator of overall individual health. Identifying factors related to cognitive status, especially in later life, is of major importance. We concentrate on the analysis of the temporal evolution of cognitive abilities in the elderly population. We propose to model the individual cognitive functioning as a multidimensional latent process that accounts also for the effects of individual-specific characteristics (gender, age, and years of education). The proposed model is specified within the generalized linear latent variable framework, and its efficient estimation is obtained using a recent approximation technique, called dimensionwise quadrature. It provides a fast and streamlined approximate inference for complex models, with better or no degradation in accuracy compared with standard techniques. The methodology is applied to the cognitive assessment data from the Health and Retirement Study combined with the Asset and Health Dynamic study in the years between 2006 and 2010. We evaluate the temporal relationship between two dimensions of cognitive functioning, that is, episodic memory and general mental status. We find a substantial influence of the former on the evolution of the latter, as well as evidence of severe consequences on both cognitive abilities among less-educated and older individuals.

Late-Life Cognitive Trajectories and their Associated Lifestyle Factors

Significant variability exists in the trajectories of late-life cognitive decline; however, their associated lifestyle factors remain less studied. We examined these trajectories among elderly participants from the recent five waves (at three-year intervals) of the Chinese Longitudinal Healthy Longevity Study (CLHLS) from 2002 to 2014. Participants from this cohort were included if they completed at least four waves of measurements. Mini-Mental State Examination (MMSE) scores, demographics, medical diagnoses (e.g., hypertension, diabetes, and heart disease), and lifestyle-related information (e.g., smoking, drinking alcohol, and exercise) were collected from participants (N = 2,584; mean age at baseline = 73.3) at least four times across 12 years. MMSE scores were entered into a latent class mixed model analysis. Subsequently, demographic, medical, and lifestyle predictors were entered into multinomial logistic regression models to predict the trajectories. One of the four emerged classes (no decline) was characterized by an absence of cognitive decline; the other three exhibited various degrees of cognitive decline. The inclusion of lifestyle factors significantly improved the prediction of the different trajectories, above and beyond demographics and medical variables; the 'no decline' class was significantly more likely to report exercising regularly. Changes in cognitive functioning across the late-life period are characterized by multiple trajectories. Cognitive decline is not inevitable across the late-life period; the absence of such cognitive decline is partly explained by certain lifestyle factors.

Does the association between cognition and education differ between older adults with gradual or rapid trajectories of cognitive decline?

Education is associated with improved baseline cognitive performance in older adults, but the association with maintenance of cognitive function is less clear. Education may be associated with different types of active cognitive reserve in those following different cognitive trajectories. We used data on n = 5642 adults aged >60 from the English Longitudinal Study of Aging (ELSA) over 5 waves (8 years). We used growth mixture models to test if the association between educational attainment and rate of change in verbal fluency or immediate recall varied by latent class trajectory. For recall, 91.5% (n = 5164) of participants were in a gradual decline class and 8.5% (n = 478) in a rapid decline class. For fluency, 90.0% (n = 4907) were in a gradual decline class and 10.0% (n = 561) were in a rapid decline class. Educational attainment was associated with improved baseline performance for both verbal fluency and recall. In the rapidly declining classes, educational attainment was not associated with rate of change for either outcome. In the verbal fluency gradual decline class, education was associated with higher (an additional 0.05-0.38 words per 2 years) or degree level education (an additional 0.04-0.42 words per 2 years) when compared to those with no formal qualifications. We identified no evidence of a protective effect of education against rapid cognitive decline. There was some evidence of active cognitive reserve for verbal fluency but not recall, which may reflect a small degree of domain-specific protection against age-related cognitive decline.

Cognitive decline trajectories and influencing factors in China: A non-normal growth mixture model analysis

BACKGROUND

With the increase in the aging population worldwide, cognitive decline has become an important research topic. The purpose of this study is to examine the cognitive development trajectories and influencing factors of different latent classes of Chinese elderly people. This will provide us with effective guidance for prevention and intervention.

METHODS

Four waves of data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were collected and included 2440 Chinese elderly individuals. The cognitive function of elderly individuals was measured using the Mini Mental State Examination (MMSE). A nonnormal Growth Mixture model (GMM) with five time-invariant covariates was used to identify the different trajectories of cognitive decline in elderly individuals.

RESULTS

Three latent decline trajectory groups were identified: stable cognitive group (SCG), high initial level - cognitive decline group (HIL-CDG), and high initial level - cognitive decline group (LIL-CDG). Elderly women were more likely to be assigned to a lower level subgroup than men. People who smoked and played cards or mahjong were more likely to be assigned to a cognitively stable group.

CONCLUSION

Education may help raise the upper limit of cognition. Smoking may impair cognitive upper limit. A small amount of alcohol intake and participation in cognitive and physical activities may help the elderly to delay cognitive decline in their later years.

Race, APOEɛ4, and Long-Term Cognitive Trajectories in a Biracial Population Sample

BACKGROUND

The association of the APOEɛ4 allele with incident Alzheimer's dementia is higher among European Americans (EAs) than African Americans (AAs), but similar for the rate of cognitive decline.

OBJECTIVE

To examine the racial differences in the association of the APOEɛ4 allele with incident Alzheimer's dementia and cognitive decline.

METHODS

Using a population-based sample of 5,117 older adults (66% AAs and 63% females), we identified cognitive trajectory groups from a latent class mixed model and examined the association of the APOEɛ4 allele with these groups.

RESULTS

The frequency of the APOEɛ4 allele was higher among AAs than EAs (37% versus 26%). Four cognitive trajectories were identified: slow, mild, moderate, and rapid. Overall, AAs had a lower baseline global cognition than EAs, and a higher proportion had rapid (7% versus 5%) and moderate (20% versus 15%) decline, but similar mild (44% versus 46%), and lesser slow (29% versus 34%) decline compared to EAs. Additionally, 25% of AAs (13% of EAs) with mild and 5% (<1% of EAs) with slow decline were diagnosed with incident Alzheimer's dementia. The APOEɛ4 allele was associated with higher odds of rapid and moderate decline compared to slow decline among AAs and EAs, but not with mild decline.

CONCLUSIONS

AAs had lower cognitive levels and were more likely to meet the cognitive threshold for Alzheimer's dementia among mild and slow decliners, explaining the attenuated association of the ɛ4 allele with incident Alzheimer's dementia among AAs.

Regional differences in beta amyloid plaque deposition and variable response to midlife environmental enrichment in the cortex of APP/PS1 mice

Environmentally enriched housing conditions can increase performance on cognitive tasks in APP/PS1 mice; however, the potential effects of environmental enrichment (EE) on disease modification in terms of pathological change are inconclusive. We hypothesized that previous contrasting findings may be attributable to regional differences in susceptibility to amyloid beta (Aβ) plaque deposition in cortical regions that are functionally associated with EE. We characterized fibrillar plaque deposition in 6, 12, and 18-22 months old APP/PS1 mice in the prefrontal (PFC), somatosensory (SS2), and primary motor cortex (M1). We found a significant increase in plaque load between 6 and 12 months in all regions. In animals over 12 months, only the PFC region continued to significantly accumulate plaques. Additionally, 12 months old animals subjected to 6 months of EE showed improved spatial navigation and had significantly fewer plaques in M1 and SS2, but not in the PFC. These findings suggest that the PFC region is selectively susceptible to Aβ deposition and less responsive to the attenuating effects of EE. In contrast, M1 and SS2 regions plateau with respect to Aβ deposition by 12 months of age and are susceptible to amyloid pathology modification by midlife EE.

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Background

Cognitive aging is a dynamic process in late life with significant heterogeneity across individuals.

Objective

To review the evidence for latent classes of cognitive trajectories and to identify the associated predictors and outcomes.

Methods

A systematic search was performed in MEDLINE and EMBASE for articles that identified two or more cognitive trajectories in adults. The study was conducted following the PRISMA statement.

Results

Thirty-seven studies were included, ranging from 219 to 9,704 participants, with a mean age of 60 to 93.4 years. Most studies (n = 30) identified distinct cognitive trajectories using latent class growth analysis. The trajectory profile commonly consisted of three to four classes with progressively decreasing baseline and increasing rate of decline-a 'stable-high' class characterized as maintenance of cognitive function at high level, a 'minor-decline' class or 'stable-medium' class that declines gradually over time, and a 'rapid-decline' class with the steepest downward slope. Generally, membership of better classes was predicted by younger age, being female, more years of education, better health, healthier lifestyle, higher social engagement and lack of genetic risk variants. Some factors (e.g., education) were found to be associated with cognitive function over time only within individual classes.

Conclusion

Cognitive aging in late life is a dynamic process with significant inter-individual variability. However, it remains unclear whether similar patterns of cognitive aging are observed across all cognitive domains. Further research into unique factors which promote the maintenance of high-cognitive function is needed to help inform public policy.

Longitudinal trajectories of hippocampal volume in middle to older age community dwelling individuals

Understanding heterogeneity in brain aging trajectories is important to estimate the extent to which aging outcomes can be optimized. Although brain changes in late life are well-characterized, brain changes in middle age are not well understood. In this study, we investigated hippocampal change in a generally healthy community-living population of middle (n = 421, mean age 47.2 years) and older age (n = 411, mean age 63.0 years) individuals, over a follow-up of up to 12 years. Manually traced hippocampal volumes were analyzed using multilevel models and latent class analysis to investigate longitudinal aging trajectories and laterality and sex effects, and to identify subgroups that follow different aging trajectories. Hippocampal volumes decreased on average by 0.18%/year in middle age and 0.3%/year in older age. Men tended to experience steeper declines than women in middle age only. Three subgroups of individuals following different trajectories were identified in middle age and 2 in older age. Contrary to expectations, the subgroup containing two-thirds of older age participants maintained stable hippocampal volumes across the follow-up.

Heterogeneity in the dynamic change of cognitive function among older Chinese people: A growth mixture model

OBJECTIVES

Our aim is to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation.

METHODS

We obtained data from five waves (2002, 2005, 2008, 2011, 2014) of the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied growth mixture modeling (GMM) to identify heterogeneous subpopulations and potential risk factors.

RESULTS

Our sample included 3859 older adults, 1387 (48.7%) male and 1974 (51.2%) female with age range of 62 to 108 (average of 74.5) at initial survey. Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: (a) cognitively stable (87.8%) group with 0.49 C-MMSE points decline per 3 years, and (b) cognitively declining (12.2%) group with 6.03 C-MMSE points decline per 3 years. Of note, cognitive activities were protective, and hearing and visual impairments were risk factors in both groups. Diabetes, hypertension, stroke and cardiovascular disease were associated with cognitive decline in the cognitively declining group. Physical activities, and intake of fresh vegetables, fruits, and fish products were protective in the cognitively stable group.

CONCLUSIONS

Using GMM, we identified heterogeneity in trajectories of cognitive change in older Chinese people. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.

Trajectories of cognitive function and their determinants in older people: 12 years of follow-up in the Chinese Longitudinal Healthy Longevity Survey

BACKGROUND

Cognitive decline in advanced age is closely related to dementia. The trajectory of cognitive function in older Chinese is yet to be fully investigated. We aimed to investigate the trajectories of cognitive function in a nationally representative sample of older people living in China and to explore the potential determinants of these trajectories.

METHODS

This study included 2,038 cognitively healthy persons aged 65-104 years at their first observation in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2014. Cognitive function was measured using the Chinese version of the Mini-Mental State Examination (MMSE). Group-based trajectory modeling was used to identify potential heterogeneity of longitudinal changes over the 12 years and to investigate associations between baseline predictors of group membership and these trajectories.

RESULTS

Three trajectories were identified according to the following types of changes in MMSE scores: slow decline (14.0%), rapid decline (4.5%), and stable function (81.5%). Older age, female gender, having no schooling, a low frequency of leisure activity, and a low baseline MMSE score were associated with the slow decline trajectory. Older age, body mass index (BMI) less than 18.5 kg/m2, and having more than one cardiovascular disease (CVD) were associated with the rapid decline trajectory.

CONCLUSION

Three trajectories of cognitive function were identified in the older Chinese population. The identified determinants of these trajectories could be targeted for developing prevention and intervention strategies for dementia.

Longitudinal Sensitivity of Alzheimer's Disease Severity Staging

Understanding Alzheimer's disease (AD) dynamics is essential in diagnosis and measuring progression for clinical decision-making; however, clinical instruments are imperfect at classifying true disease stages. This research evaluates sensitivity and determinants of AD stage changes longitudinally using current classifications of "mild," "moderate," and "severe" AD, using Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), and the Clinical Dementia Rating-Sum of Boxes (CDR-SB) thresholds. Age and pre-progression rate were significant determinants of AD progression using MMSE alone to stage AD, and pre-progression was found to impact disease progression with CDR-SB. Sensitivity of these instruments for identifying clinical stages of AD to correctly staging a "moderate" level of disease severity for outcomes MMSE, CDR-SB, and ADAS-Cog was 92%, 78%, and 92%, respectively. This research derives longitudinal sensitivity of clinical instruments used to stage AD useful for clinical decision-making. The MMSE and ADAS-Cog provided adequate sensitivity to classify AD stages.

Cognitive and functional progression of dementia in two longitudinal studies

OBJECTIVES

Previous studies have identified several subgroups (ie, latent trajectories) with distinct disease progression among people with dementia. However, the methods and results were not always consistent. This study aims to perform a coordinated analysis of latent trajectories of cognitive and functional progression in dementia across two datasets.

METHODS

Included and analyzed using the same statistical approach were 1628 participants with dementia from the US National Alzheimer's Coordinating Center (NACC) and 331 participants with dementia from the Dutch Clinical Course of Cognition and Comorbidity study (4C-Study). Trajectories of cognition and instrumental activities of daily living (IADL) were modeled jointly in a parallel-process growth mixture model.

RESULTS

Cognition and IADL tended to decline in unison across the two samples. Slow decline in both domains was observed in 26% of the US sample and 74% of the Dutch sample. Rapid decline in cognition and IADL was observed in 7% of the US sample and 26% of the Dutch sample. The majority (67%) of the US sample showed moderate cognitive decline and rapid IADL decline.

CONCLUSIONS

Trajectories of slow and rapid dementia progression were identified in both samples. Despite using the same statistical methods, the number of latent trajectories was not replicated and the relative class sizes differed considerably across datasets. These results call for careful consideration when comparing progression estimates in the literature. In addition, the observed discrepancy between cognitive and functional decline stresses the need to monitor dementia progression across multiple domains.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50

Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Cognitive ageing trajectories and mortality of Chinese oldest-old

OBJECTIVE

This study aims to identify distinctive cognitive trajectories jointly with mortality probabilities and to explore factors related to the particular trajectories of cognitive ageing in China.

METHOD

6842 individuals aged 80 years and above from 7 waves of the Chinese Longitudinal Healthy Longevity Survey were assessed with the Mini-Mental State Examination for up to 16 years. A group-based trajectory model was used to jointly estimate cognitive ageing and mortality trajectories; and to explore the factors related to membership of the trajectory groups.

RESULTS

A four-group model best fit the data. For all groups, the cognitive function declined with age according to different rates. Group 4, 3, 2, and 1 showed slow (prevalence 52.8%), moderate (31.1%), progressive (12.6%) and rapid (3.5%) cognitive decline, respectively. Mortality probability trajectories followed a hierarchy in consistence with cognitive trajectories approximately. Females, illiteracy, and those born in rural areas were less likely to belong to the most favorable trajectory group.

CONCLUSIONS

The heterogeneity of cognitive ageing was identified among Chinese oldest-old. Childhood socioeconomic status, especially education, was associated with the rate of cognitive decline.

An examination of the heterogeneity in the pattern and association between rates of change in grip strength and global cognition in late life. A multivariate growth mixture modelling approach

Background

previous research has demonstrated how older adults exhibit different patterns of change in cognitive and physical functioning, suggesting differences in the underlying causal processes.

Objective

to (i) identify subgroups of older adults that best account for different patterns of longitudinal change in performance on global cognition and grip strength, (ii) examine the interrelationship between global cognition and grip strength trajectories within these subgroups and (iii) identify demographic and health-related markers of class membership.

Methods

multivariate growth mixture models (GMM) were used to identify groups of individuals with similar developmental trajectories of muscle strength measured by grip strength, and global cognition measured by Mini Mental State Examination (MMSE).

Results

GMM analyses indicated high, moderate and low functioning groups. Individuals in the high and moderate classes demonstrated better cognitive and physical functioning at the start of the study and less decline than those in the low functioning group. Notably, cognitive performance was related to physical functioning at study entry only among individuals in the low functioning group.

Conclusion

the study demonstrates the applicability of the multivariate GMM to achieve a better understanding of the heterogeneity of various aging related processes.

Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data

Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.

Cognitive and functional progression in Alzheimer disease: A prediction model of latent classes

OBJECTIVE

We sought to replicate a previously published prediction model for progression, developed in the Cache County Dementia Progression Study, using a clinical cohort from the National Alzheimer's Coordinating Center.

METHODS

We included 1120 incident Alzheimer disease (AD) cases with at least one assessment after diagnosis, originating from 31 AD centres from the United States. Trajectories of the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating sum of boxes (CDR-sb) were modelled jointly over time using parallel-process growth mixture models in order to identify latent classes of trajectories. Bias-corrected multinomial logistic regression was used to identify baseline predictors of class membership and compare these with the predictors found in the Cache County Dementia Progression Study.

RESULTS

The best-fitting model contained 3 classes: Class 1 was the largest (63%) and showed the slowest progression on both MMSE and CDR-sb; classes 2 (22%) and 3 (15%) showed moderate and rapid worsening, respectively. Significant predictors of membership in classes 2 and 3, relative to class 1, were worse baseline MMSE and CDR-sb, higher education, and lack of hypertension. Combining all previously mentioned predictors yielded areas under the receiver operating characteristic curve of 0.70 and 0.75 for classes 2 and 3, respectively, relative to class 1.

CONCLUSIONS

Our replication study confirmed that it is possible to predict trajectories of progression in AD with relatively good accuracy. The class distribution was comparable with that of the original study, with most individuals being members of a class with stable or slow progression. This is important for informing newly diagnosed AD patients and their caregivers.

A longitudinal study of cognitive trajectories and its factors for Koreans aged 60 and over: A latent growth mixture model

OBJECTIVE

The purpose of this study is twofold: first, to identify cognitive trajectories of older Koreans in a population-based longitudinal panel survey and, second, to investigate the main characteristics of the identified heterogeneous classes of cognitive trajectories.

METHODS

Data came from 2445 cognitively healthy persons aged 60 or older in the 2006 to 2012 Korean Longitudinal Study of Aging. Using Korean-mini mental status examination (K-MMSE) as a measure of global cognitive function, the latent growth mixture modeling approach examined potential heterogeneity of longitudinal changes over the 6 years.

RESULTS

This study found that older Koreans reported an average K-MMSE score of 27 at baseline and experienced a cognitive decline every 2 years by -1.6 (2006-2008) and -1.2 (2008-2010), followed by a slight increase of 0.7 in 2012. Results from the latent growth mixture modeling analysis indicated that there were 2 heterogeneous classes of longitudinal changes in the K-MMSE over a period of 6 years: class 1 with stable cognitive function and class 2 with sharp cognitive decline over time. The sharp decline was found among those older in age and with higher level of depression at baseline. On the contrary, being male, higher education, active social engagement, and regular exercise were main characteristics of stable cognitive function.

CONCLUSION

As the first to examine cognitive trajectories among older Koreans, this study highlighted heterogeneity of cognitive trajectories in the population that should be considered for developing differential intervention strategies aimed at both promoting healthy brain and delaying/preventing cognitive decline.

A longitudinal study of cognitive trajectories in Mexican Americans age 75 and older

OBJECTIVE

To identify distinct trajectories for global cognition, memory, and non-memory domains among Mexican American adults 75 years of age and older.

METHODS

The final sample included 1336 participants of the Hispanic Established Population for the Epidemiologic Study of the Elderly observed during four Waves from 2004-2005 to 2012-2013. Latent class growth curve models were used to identify distinct trajectories for global cognition, memory, and non-memory.

RESULTS

Three trajectory classes were identified for global cognition, memory, and non-memory domains. Nearly 31% of the final sample maintained high global cognition (persistent high), 52.6% experienced slight decline (decline but high), and 15% experienced severe decline in global cognition (decline to low). Over 95% of participants classified in the decline to low trajectory for global cognition were also classified as decline to low for memory and non-memory. This high level of consistency for memory and non-memory domains was observed for the decline but high (97.0%) and persistent high (93.7%) trajectory classes.

CONCLUSIONS

These results indicate that the majority of Mexican American older adults will experience varying degrees of cognitive decline. However, a substantial proportion of older Mexican Americans are able to maintain high cognitive functioning into advanced age despite the high prevalence of risk factors for cognitive decline in this population. Copyright © 2016 John Wiley & Sons, Ltd.

[Formula: see text]Practice effects and longitudinal cognitive change in clinically normal older adults differ by Alzheimer imaging biomarker status

OBJECTIVE

The objective of this study was to examine practice effects and longitudinal cognitive change in 190 clinically normal elderly classified according to a two-feature biomarker model for Alzheimer's disease.

METHODS

All participants completed neuropsychological testing, MRI, FDG-PET, and PiB-PET at their baseline evaluation. We divided participants into four groups based on neuroimaging measures of amyloid (A+ or A-) and neurodegeneration (N+ or N-) and reexamined cognition at 15- and 30-month intervals.

RESULTS

The A-N- group showed significant improvements in the memory and global scores. The A+N- group also showed significant improvements in the memory and global scores as well as attention. The A-N+ group showed a significant decline in attention at 30 months. The A+N+ group showed significant improvements in memory and the global score at 15 months followed by a significant decline in the global score at 30 months.

CONCLUSION

Amyloidosis in the absence of neurodegeneration did not have an adverse impact on practice effects or the 30-month cognitive trajectories. In contrast, participants with neurodegeneration (either A-N+ or A+N+) had worse performance at the 30-month follow-up. Our results show that neurodegeneration has a more deleterious effect on cognition than amyloidosis in clinically normal individuals.

Empirically Defining Trajectories of Late-Life Cognitive and Functional Decline

BACKGROUND

Alzheimer's disease (AD) is associated with variable cognitive and functional decline, and it is difficult to predict who will develop the disease and how they will progress.

OBJECTIVE

This exploratory study aimed to define latent classes from participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database who had similar growth patterns of both cognitive and functional change using Growth Mixture Modeling (GMM), identify characteristics associated with those trajectories, and develop a decision tree using clinical predictors to determine which trajectory, as determined by GMM, individuals will most likely follow.

METHODS

We used ADNI early mild cognitive impairment (EMCI), late MCI (LMCI), AD dementia, and healthy control (HC) participants with known amyloid-β status and follow-up assessments on the Alzheimer's Disease Assessment Scale - Cognitive Subscale or the Functional Activities Questionnaire (FAQ) up to 24 months postbaseline. GMM defined trajectories. Classification and Regression Tree (CART) used certain baseline variables to predict likely trajectory path.

RESULTS

GMM identified three trajectory classes (C): C1 (n = 162, 13.6%) highest baseline impairment and steepest pattern of cognitive/functional decline; C3 (n = 819, 68.7%) lowest baseline impairment and minimal change on both; C2 (n = 211, 17.7%) intermediate pattern, worsening on both, but less steep than C1. C3 had fewer amyloid- or apolipoprotein-E ɛ4 (APOE4) positive and more healthy controls (HC) or EMCI cases. CART analysis identified two decision nodes using the FAQ to predict likely class with 82.3% estimated accuracy.

CONCLUSIONS

Cognitive/functional change followed three trajectories with greater baseline impairment and amyloid and APOE4 positivity associated with greater progression. FAQ may predict trajectory class.

Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations

BACKGROUND

The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia.

OBJECTIVES

To determine the diagnostic accuracy of the Mini-Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia.

SEARCH METHODS

We searched the specialised register of the Cochrane Dementia and Cognitive Improvement Group, MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), LILACS (BIREME), ALOIS, BIOSIS previews (Thomson Reuters Web of Science), and Web of Science Core Collection, including the Science Citation Index and the Conference Proceedings Citation Index (Thomson Reuters Web of Science). We also searched specialised sources of diagnostic test accuracy studies and reviews: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). We attempted to locate possibly relevant but unpublished data by contacting researchers in this field. We first performed the searches in November 2012 and then fully updated them in May 2014. We did not apply any language or date restrictions to the electronic searches, and we did not use any methodological filters as a method to restrict the search overall.

SELECTION CRITERIA

We included studies that compared the 11-item (maximum score 30) MMSE test (at any cut point) in people who had not undergone prior testing versus a commonly accepted clinical reference standard for all-cause dementia and subtypes (Alzheimer disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia). Clinical diagnosis included all-cause (unspecified) dementia, as defined by any version of the Diagnostic and Statistical Manual of Mental Disorders (DSM); International Classification of Diseases (ICD) and the Clinical Dementia Rating.

DATA COLLECTION AND ANALYSIS

At least three authors screened all citations.Two authors handled data extraction and quality assessment. We performed meta-analysis using the hierarchical summary receiver-operator curves (HSROC) method and the bivariate method.

MAIN RESULTS

We retrieved 24,310 citations after removal of duplicates. We reviewed the full text of 317 full-text articles and finally included 70 records, referring to 48 studies, in our synthesis. We were able to perform meta-analysis on 28 studies in the community setting (44 articles) and on 6 studies in primary care (8 articles), but we could not extract usable 2 x 2 data for the remaining 14 community studies, which we did not include in the meta-analysis. All of the studies in the community were in asymptomatic people, whereas two of the six studies in primary care were conducted in people who had symptoms of possible dementia. We judged two studies to be at high risk of bias in the patient selection domain, three studies to be at high risk of bias in the index test domain and nine studies to be at high risk of bias regarding flow and timing. We assessed most studies as being applicable to the review question though we had concerns about selection of participants in six studies and target condition in one study.The accuracy of the MMSE for diagnosing dementia was reported at 18 cut points in the community (MMSE score 10, 14-30 inclusive) and 10 cut points in primary care (MMSE score 17-26 inclusive). The total number of participants in studies included in the meta-analyses ranged from 37 to 2727, median 314 (interquartile range (IQR) 160 to 647). In the community, the pooled accuracy at a cut point of 24 (15 studies) was sensitivity 0.85 (95% confidence interval (CI) 0.74 to 0.92), specificity 0.90 (95% CI 0.82 to 0.95); at a cut point of 25 (10 studies), sensitivity 0.87 (95% CI 0.78 to 0.93), specificity 0.82 (95% CI 0.65 to 0.92); and in seven studies that adjusted accuracy estimates for level of education, sensitivity 0.97 (95% CI 0.83 to 1.00), specificity 0.70 (95% CI 0.50 to 0.85). There was insufficient data to evaluate the accuracy of the MMSE for diagnosing dementia subtypes.We could not estimate summary diagnostic accuracy in primary care due to insufficient data.

AUTHORS' CONCLUSIONS

The MMSE contributes to a diagnosis of dementia in low prevalence settings, but should not be used in isolation to confirm or exclude disease. We recommend that future work evaluates the diagnostic accuracy of tests in the context of the diagnostic pathway experienced by the patient and that investigators report how undergoing the MMSE changes patient-relevant outcomes.

Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies

Age-related cognitive changes often include difficulties in retrieving memories, particularly those that rely on personal experiences within their temporal and spatial contexts (i.e., episodic memories). This decline may vary depending on the studied phase (i.e., encoding, storage or retrieval), according to inter-individual differences, and whether we are talking about normal or pathological (e.g., Alzheimer disease; AD) aging. Such cognitive changes are associated with different structural and functional alterations in the human neural network that underpins episodic memory. The prefrontal cortex is the first structure to be affected by age, followed by the medial temporal lobe (MTL), the parietal cortex and the cerebellum. In AD, however, the modifications occur mainly in the MTL (hippocampus and adjacent structures) before spreading to the neocortex. In this review, we will present results that attempt to characterize normal and pathological cognitive aging at multiple levels by integrating structural, behavioral, inter-individual and neuroimaging measures of episodic memory.

Cognitive Aging Trajectories and Burdens of Disability, Hospitalization and Nursing Home Admission Among Community-living Older Persons

BACKGROUND

The course of cognitive aging has demonstrated substantial heterogeneity. This study attempted to identify distinctive cognitive trajectories and examine their relationship with burdens of disability, hospitalization, and nursing home admission.

METHODS

Seven hundred and fifty-four community-living persons aged 70 years or older in the Yale Precipitating Events Project were assessed with the Mini-Mental State Examination every 18 months for up to 108 months. A group-based trajectory model was used to determine cognitive aging trajectories while adjusting for age, sex, and education. Cumulative burden of disabilities, hospitalizations, and nursing home admissions over 141 months associated with the cognitive trajectories were evaluated using a generalized estimating equation Poisson model.

RESULTS

Five distinct cognitive trajectories were identified, with about a third of participants starting with high baseline cognitive function and demonstrating No decline during the follow-up period. The remaining participants diverged with Minimal (prevalence 41%), Moderate (16%), Progressive (8%), and Rapid (3%) cognitive decline. Participants with No decline incurred the lowest incidence rates (per 1,000 person-months) of disability in activities of daily living (ADL; 75, 95% confidence intervals: 60-95) and instrumental ADL (492, 453-535), hospitalization (29, 26-33) and nursing home admission (18, 12-27), whereas participants on the Rapid trajectory experienced the greatest burden of ADL disability (612, 595-758) and those on the Progressive trajectory had the highest nursing home admission (363, 292-451).

CONCLUSIONS

Community-living older persons follow distinct cognitive aging trajectories and experience increasing burdens of disability, hospitalization, and nursing home placement as they age, with greater burdens for those on a declining cognitive trajectory.

Medicare Utilization and Expenditures Around Incident Dementia in a Multiethnic Cohort

BACKGROUND

Few studies have examined patterns of health care utilization and costs during the period around incident dementia.

METHODS

Participants were drawn from the Washington Heights-Inwood Columbia Aging Project, a multiethnic, population-based, prospective study of cognitive aging of Medicare beneficiaries in a geographically defined area of northern Manhattan. Medicare utilization and expenditure were examined in individuals with clinically diagnosed dementia from 2 years before until 2 years after the initial diagnosis. A sample of non-demented individuals who were matched on socio-demographic and clinical characteristics at study enrollment was used as controls. Multivariable regression analysis estimated effects on Medicare utilization and expenditures associated with incident dementia.

RESULTS

During the 2 years before incident dementia, rates of inpatient admissions and outpatient visits were similar between dementia patients and non-demented controls, but use of home health and skilled nursing care and durable medical equipment were already higher in dementia patients. Results showed a small but significant excess increase associated with incident dementia in inpatient admissions but not in other areas of care. In the 2 years before incident dementia, total Medicare expenditures were already higher in dementia patients than in non-demented controls. But we found no excess increases in Medicare expenditures associated with incident dementia.

CONCLUSIONS

Demand for medical care already is increasing and costs are higher at the time of incident dementia. There was a small but significant excess risk of inpatient admission associated with incident dementia.

Latent classes of course in Alzheimer's disease and predictors: the Cache County Dementia Progression Study

OBJECTIVE

Several longitudinal studies of Alzheimer's disease (AD) report heterogeneity in progression. We sought to identify groups (classes) of progression trajectories in the population-based Cache County Dementia Progression Study (N = 328) and to identify baseline predictors of membership for each group.

METHODS

We used parallel-process growth mixture models to identify latent classes of trajectories on the basis of Mini-Mental State Exam (MMSE) and Clinical Dementia Rating sum of boxes scores over time. We then used bias-corrected multinomial logistic regression to model baseline predictors of latent class membership. We constructed receiver operating characteristic curves to demonstrate relative predictive utility of successive sets of predictors.

RESULTS

We fit four latent classes; class 1 was the largest (72%) and had the slowest progression. Classes 2 (8%), 3 (11%), and 4 (8%) had more rapid worsening. In univariate analyses, longer dementia duration, presence of psychosis, and worse baseline MMSE and Clinical Dementia Rating sum of boxes were associated with membership in class 2, relative to class 1. Lower education was associated with membership in class 3. In the multivariate model, only MMSE remained a statistically significant predictor of class membership. Receiver operating characteristic areas under the curve were 0.98, 0.88, and 0.67, for classes 2, 3, and 4 relative to class 1.

CONCLUSIONS

Heterogeneity in AD course can be usefully characterized using growth mixture models. The majority belonged to a class characterized by slower decline than is typically reported in clinical samples. Class membership could be predicted using baseline covariates. Further study may advance our prediction of AD course at the population level and in turn shed light on the pathophysiology of progression.

Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia

OBJECTIVE

To examine the relation of performance on brief cognitive tests to development of clinically diagnosed Alzheimer disease (AD) dementia over the following 18 years in a sample of African Americans and European Americans.

METHODS

A composite cognitive test score based on tests of episodic memory, executive function, and global cognition was constructed in a prospective population-based sample of 2,125 participants (55% African American and 61% female) aged 65 years and older residing in 4 Chicago neighborhoods. Time before AD dementia diagnosis was categorized into 6 groups corresponding to data collection periods: 0.1-0.9, 1.0-3.9, 4.0-6.9, 7.0-9.9, 10.0-12.9, and 13.0-17.9 years.

RESULTS

Of 2,125 participants without clinical AD dementia, 442 (21%) developed clinical AD dementia over 18 years of follow-up. Lower composite cognitive test scores were associated with the development of AD dementia over the duration of the study. The magnitude of association between composite cognitive test score and development of AD dementia increased from an odds ratio of 3.39 (95% confidence interval 1.72, 6.67; p < 0.001) at 13.0-17.9 years to 9.84 (95% confidence interval 7.41, 13.06; p < 0.001) at 0.1-0.9 years, per SD increment. These associations were consistently larger among European Americans than among African Americans. Performance on individual cognitive tests of episodic memory, executive function, and global cognition also significantly predicted the development of AD dementia, with associations exhibiting a similar trend over 18 years.

CONCLUSIONS

Our findings suggest that cognitive impairment may manifest in the preclinical phase of AD dementia substantially earlier than previously established.

Effect of common neuropathologies on progression of late life cognitive impairment

Brain pathologies of Alzheimer's (AD), cerebrovascular, and Lewy body diseases are common in old age, but the relationship of these pathologies with progression from normal cognitive function to the various stages of cognitive impairment is unknown. In this study, we fit latent Markov models from longitudinal cognitive data to empirically derive 3 latent stages corresponding to no impairment, mild impairment, and moderate impairment; then, we examined the associations of common neuropathologies with the rates of transition among these stages. Cognitive and neuropathological data were available from 653 autopsied participants in 2 ongoing cohort studies of aging who were cognitively healthy at baseline (mean baseline age 79.1 years) and had longitudinal cognitive data. On average, participants in these analyses developed mild impairment 5 years after enrollment, progressed to moderate impairment after an additional 3.4 years, and stayed impaired for 2.8 years until death. AD and chronic macroscopic infarcts were associated with a higher risk of progression to mild impairment and subsequently to moderate impairment. By contrast, Lewy bodies were associated only with progression from mild to moderate impairment. The 5-year probability of progression to mild or moderate impairment was 20% for persons without any of these 3 pathologies, 38% for AD only, 51% for AD and macroscopic infarcts, and 56% for AD, infarcts, and Lewy bodies. Thus, the presence of AD pathology alone nearly doubles the risk of developing cognitive impairment in late life, and the presence of multiple pathologies further increases this risk over multiple years before death.

Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons

Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual’s health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions.

An empirically derived composite cognitive test score with improved power to track and evaluate treatments for preclinical Alzheimer's disease

BACKGROUND

There is growing interest in the evaluation of preclinical Alzheimer's disease (AD) treatments. As a result, there is a need to identify a cognitive composite that is sensitive to track preclinical AD decline to be used as a primary endpoint in treatment trials.

METHODS

Longitudinal data from initially cognitively normal, 70- to 85-year-old participants in three cohort studies of aging and dementia from the Rush Alzheimer's Disease Center were examined to empirically define a composite cognitive endpoint that is sensitive to detect and track cognitive decline before the onset of cognitive impairment. The mean-to-standard deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of cognitive tests/subtests drawn from the neuropsychological battery in cognitively normal participants who subsequently progressed to clinical stages of AD during 2- and 5-year periods, using data from those who remained unimpaired during the same period to correct for aging and practice effects. Combinations that performed well were then evaluated for representation of relevant cognitive domains, robustness across individual years before diagnosis, and occurrence of selected items within top performing combinations.

RESULTS

The optimal composite cognitive test score comprised seven cognitive tests/subtests with an MSDR = 0.964. By comparison, the most sensitive individual test score was Logical Memory Delayed Recall with an MSDR = 0.64.

CONCLUSIONS

We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared with the most sensitive single test item to track preclinical AD decline and evaluate preclinical AD treatments. We are confirming the power of the composite in independent cohorts and with other analytical approaches, which may result in refinements, have designated it as the primary endpoint in the Alzheimer's Prevention Initiative's preclinical treatment trials for individuals at high imminent risk for developing symptoms due to late-onset AD.

The Alzheimer's prevention initiative composite cognitive test score: sample size estimates for the evaluation of preclinical Alzheimer's disease treatments in presenilin 1 E280A mutation carriers

OBJECTIVE

To identify a cognitive composite that is sensitive to tracking preclinical Alzheimer's disease decline to be used as a primary end point in treatment trials.

METHOD

We capitalized on longitudinal data collected from 1995 to 2010 from cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers from the world's largest known early-onset autosomal dominant Alzheimer's disease kindred to identify a composite cognitive test with the greatest statistical power to track preclinical Alzheimer's disease decline and estimate the number of carriers age 30 years and older needed to detect a treatment effect in the Alzheimer's Prevention Initiative's (API) preclinical Alzheimer's disease treatment trial. The mean-to-standard-deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of 1 to 7 cognitive tests/subtests drawn from the neuropsychological test battery in cognitively unimpaired mutation carriers during a 2- and 5-year follow-up period (n = 78 and 57), using data from noncarriers (n = 31 and 56) during the same time period to correct for aging and practice effects. Combinations that performed well were then evaluated for robustness across follow-up years, occurrence of selected items within top-performing combinations, and representation of relevant cognitive domains.

RESULTS

The optimal test combination included Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Word List Recall, CERAD Boston Naming Test (high frequency items), Mini-Mental State Examination (MMSE) Orientation to Time, CERAD Constructional Praxis, and Raven's Progressive Matrices (Set A), with an MSDR of 1.62. This composite is more sensitive than using either the CERAD Word List Recall (MSDR = 0.38) or the entire CERAD-Col battery (MSDR = 0.76). A sample size of 75 cognitively normal PSEN1 E280A mutation carriers aged 30 years and older per treatment arm allows for a detectable treatment effect of 29% in a 60-month trial (80% power, P = .05).

CONCLUSIONS

We have identified a composite cognitive test score representing multiple cognitive domains that, compared to the most sensitive single test item, has improved power to track preclinical Alzheimer's disease decline in autosomal dominant Alzheimer's disease mutation carriers and to evaluate preclinical Alzheimer's disease treatments. This API composite cognitive test score will be used as the primary end point in the first API trial in cognitively unimpaired autosomal dominant Alzheimer's disease carriers within 15 years of their estimated age at clinical onset. We have independently confirmed our findings in a separate cohort of cognitively healthy older adults who progressed to the clinical stages of late-onset Alzheimer's disease, described in a separate report, and continue to refine the composite in independent cohorts and compared with other analytic approaches.

Longitudinal trajectories of everyday function by diagnostic status

Generalized linear mixed models were used to examine longitudinal trajectories of everyday functional limitations by diagnostic stability/progression. Older adults (N = 384) were followed an average 3.6 years; participants were grouped by diagnosis at study baseline and last follow-up (normal cognition, Mild Cognitive Impairment, or dementia at each time point). At study baseline there were clear group differences; most notably among participants initially characterized as cognitively normal, those who developed Mild Cognitive Impairment or dementia over follow-up already demonstrated greater functional impairment compared with those who remained cognitively normal. Change in functional impairment progressed slowly in the early disease groups, but showed an accelerated worsening in those converting to dementia.

Building a pipeline to discover and validate novel therapeutic targets and lead compounds for Alzheimer's disease

Cognitive decline, Alzheimer's disease (AD) and other causes are major public health problems worldwide. With changing demographics, the number of persons with dementia will increase rapidly. The treatment and prevention of AD and other dementias, therefore, is an urgent unmet need. There have been considerable advances in understanding the biology of many age-related disorders that cause dementia. Gains in understanding AD have led to the development of ante-mortem biomarkers of traditional neuropathology and the conduct of several phase III interventions in the amyloid-β cascade early in the disease process. Many other intervention strategies are in various stages of development. However, efforts to date have met with limited success. A recent National Institute on Aging Research Summit led to a number of requests for applications. One was to establish multi-disciplinary teams of investigators who use systems biology approaches and stem cell technology to identify a new generation of AD targets. We were recently awarded one of three such grants to build a pipeline that integrates epidemiology, systems biology, and stem cell technology to discover and validate novel therapeutic targets and lead compounds for AD treatment and prevention. Here we describe the two cohorts that provide the data and biospecimens being exploited for our pipeline and describe the available unique datasets. Second, we present evidence in support of a chronic disease model of AD that informs our choice of phenotypes as the target outcome. Third, we provide an overview of our approach. Finally, we present the details of our planned drug discovery pipeline.

Practice effects and longitudinal cognitive change in normal aging vs. incident mild cognitive impairment and dementia in the Mayo Clinic Study of Aging

The objective of this study was to examine practice effects and longitudinal cognitive change in a population-based cohort classified as clinically normal at their initial evaluation. We examined 1390 individuals with a median age of 78.1 years and re-evaluated them up to four times at approximate 15-month intervals, with an average follow-up time of 5 years. Of the 1390 participants, 947 (69%) individuals remained cognitively normal, 397 (29%) progressed to mild cognitive impairment (MCI), and 46 (3%) to dementia. The stable normal group showed an initial practice effect in all domains which was sustained in memory and visuospatial reasoning. There was only a slight decline in attention and language after visit 3. We combined individuals with incident MCI and dementia to form one group representing those who declined. The incident MCI/dementia group showed an unexpected practice effect in memory from baseline to visit 2, with a significant decline thereafter. This group did not demonstrate practice effects in any other domain and showed a downward trajectory in all domains at each evaluation. Modeling cognitive change in an epidemiologic sample may serve as a useful benchmark for evaluating cognitive change in future intervention studies.

Cognitive change in older women using a computerised battery: a longitudinal quantitative genetic twin study

Cognitive performance is known to change over age 45, especially processing speed. Studies to date indicate that change in performance with ageing is largely environmentally mediated, with little contribution from genetics. We estimated the heritability of a longitudinal battery of computerised cognitive tests including speed measures, using a classical twin design. 324 (127 MZ, 197 DZ) female twins, aged 43–73 at baseline testing, were followed-up after 10 years, using seven measures of the Cambridge Automated Neuropsychological Test battery, four of which were measures of response latency (speed). Results were analysed using univariate and bivariate structural equation modelling. Heritability of longitudinal change was found in 5 of the 7 tests, ranging from 21 to 41 %. The genetic aetiology was remarkably stable. The first principle component of change was strongly associated with age (p < 0.001) and heritable at 47 % (27–62 %). While estimates for heritability increased in all measures over time compared to baseline, these increases were statistically non-significant. This computerised battery showed significant heritability of age-related change in cognition. Focus on this form of change may aid the search for genetic pathways involved in normal and pre-morbid cognitive ageing.

Perspectives on episodic-like and episodic memory

Episodic memory refers to the conscious recollection of a personal experience that contains information on what has happened and also where and when it happened. Recollection from episodic memory also implies a kind of first-person subjectivity that has been termed autonoetic consciousness. Episodic memory is extremely sensitive to cerebral aging and neurodegenerative diseases. In Alzheimer’s disease deficits in episodic memory function are among the first cognitive symptoms observed. Furthermore, impaired episodic memory function is also observed in a variety of other neuropsychiatric diseases including dissociative disorders, schizophrenia, and Parkinson disease. Unfortunately, it is quite difficult to induce and measure episodic memories in the laboratory and it is even more difficult to measure it in clinical populations. Presently, the tests used to assess episodic memory function do not comply with even down-sized definitions of episodic-like memory as a memory for what happened, where, and when. They also require sophisticated verbal competences and are difficult to apply to patient populations. In this review, we will summarize the progress made in defining behavioral criteria of episodic-like memory in animals (and humans) as well as the perspectives in developing novel tests of human episodic memory which can also account for phenomenological aspects of episodic memory such as autonoetic awareness. We will also define basic behavioral, procedural, and phenomenological criteria which might be helpful for the development of a valid and reliable clinical test of human episodic memory.